{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.935474\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -10.578976 analytic: -10.578976, relative error: 1.852745e-11\n",
      "numerical: -22.214779 analytic: -22.214779, relative error: 3.442400e-13\n",
      "numerical: -2.235691 analytic: -2.235691, relative error: 2.870949e-11\n",
      "numerical: -1.524646 analytic: -1.524646, relative error: 7.174921e-11\n",
      "numerical: 26.574459 analytic: 26.574459, relative error: 9.468392e-12\n",
      "numerical: 17.288594 analytic: 17.288594, relative error: 8.626152e-12\n",
      "numerical: 3.175362 analytic: 3.175362, relative error: 1.820619e-10\n",
      "numerical: -19.650392 analytic: -19.650392, relative error: 1.943314e-11\n",
      "numerical: 7.035011 analytic: 7.035011, relative error: 4.126519e-12\n",
      "numerical: 9.971273 analytic: 9.971273, relative error: 3.633976e-11\n",
      "numerical: 21.258632 analytic: 21.258632, relative error: 1.265172e-11\n",
      "numerical: 6.706171 analytic: 6.706171, relative error: 6.016699e-11\n",
      "numerical: -5.303072 analytic: -5.303072, relative error: 7.898936e-11\n",
      "numerical: -10.300421 analytic: -10.300421, relative error: 4.247734e-12\n",
      "numerical: -38.472432 analytic: -38.472432, relative error: 6.813909e-12\n",
      "numerical: -4.891487 analytic: -4.891487, relative error: 3.901640e-11\n",
      "numerical: 17.913589 analytic: 17.913589, relative error: 9.001578e-12\n",
      "numerical: -11.156870 analytic: -11.156870, relative error: 1.021899e-11\n",
      "numerical: -8.568312 analytic: -8.568312, relative error: 3.797032e-11\n",
      "numerical: 17.638895 analytic: 17.638895, relative error: 6.435467e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.935474e+00 computed in 0.168566s\n",
      "Vectorized loss: 8.935474e+00 computed in 0.013963s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.201430s\n",
      "Vectorized loss and gradient: computed in 0.007014s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 786.132194\n",
      "iteration 100 / 1500: loss 286.148677\n",
      "iteration 200 / 1500: loss 107.453226\n",
      "iteration 300 / 1500: loss 42.201040\n",
      "iteration 400 / 1500: loss 18.795530\n",
      "iteration 500 / 1500: loss 10.169113\n",
      "iteration 600 / 1500: loss 6.874834\n",
      "iteration 700 / 1500: loss 6.282358\n",
      "iteration 800 / 1500: loss 5.603511\n",
      "iteration 900 / 1500: loss 5.313929\n",
      "iteration 1000 / 1500: loss 5.277891\n",
      "iteration 1100 / 1500: loss 5.463969\n",
      "iteration 1200 / 1500: loss 6.193993\n",
      "iteration 1300 / 1500: loss 5.083069\n",
      "iteration 1400 / 1500: loss 5.716595\n",
      "That took 13.946687s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "# LinearSVM类继承了LinearClassifier类\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.365714\n",
      "validation accuracy: 0.364000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 794.171224\n",
      "iteration 100 / 1000: loss 288.046691\n",
      "iteration 200 / 1000: loss 107.696881\n",
      "iteration 300 / 1000: loss 42.045883\n",
      "iteration 400 / 1000: loss 19.015488\n",
      "iteration 500 / 1000: loss 10.281358\n",
      "iteration 600 / 1000: loss 6.596305\n",
      "iteration 700 / 1000: loss 5.950942\n",
      "iteration 800 / 1000: loss 5.671544\n",
      "iteration 900 / 1000: loss 5.543165\n",
      "iteration 0 / 1000: loss 1537.093632\n",
      "iteration 100 / 1000: loss 208.561017\n",
      "iteration 200 / 1000: loss 31.936772\n",
      "iteration 300 / 1000: loss 9.042758\n",
      "iteration 400 / 1000: loss 5.821017\n",
      "iteration 500 / 1000: loss 5.569208\n",
      "iteration 600 / 1000: loss 5.664911\n",
      "iteration 700 / 1000: loss 6.060392\n",
      "iteration 800 / 1000: loss 6.168468\n",
      "iteration 900 / 1000: loss 5.237896\n",
      "iteration 0 / 1000: loss 794.415702\n",
      "iteration 100 / 1000: loss 394794423252843217094504760660751024128.000000\n",
      "iteration 200 / 1000: loss 65256356286278216511058888203213040430954509260051252152552485306476003328.000000\n",
      "iteration 300 / 1000: loss 10786353061108020138834794151464708669251278198145769570053044716558570634360305773491540678188823935769903104.000000\n",
      "iteration 400 / 1000: loss 1782897773949707288223027311715785000282272953224570881192683303693248118877989987925510241119584076162502504612637718574790433414717755457798144.000000\n",
      "iteration 500 / 1000: loss 294698722946149098747513499414944459406752566920592932609157391998833198943938022300985918955277894592207433421212747762717621477489762613344390417797626146404918450754799229992960.000000\n",
      "iteration 600 / 1000: loss 48711338684155547334108092348731298527695653527679890304010547614864010342837082552413376613829296102350869224983477531125241464112254135188184428373701669851933986785638317951769243425008688094445759205718462824448.000000\n",
      "iteration 700 / 1000: loss 8051594159218986656385374665612010379093596519185477470576964883418173954188674250570965680073337285303492772034993281628415317798348664399499299047757413465153269205219958193807733997042579690938174823964071647238296195765339025985054256921550258176.000000\n",
      "iteration 800 / 1000: loss 1330864029935931624713132961920905696998000840967677492617545106351521875397462648168009638521344060326853877064875439092201397204106154759375476194044250837884535985810372849619619093129703458428125484810442513079001382868952940650321151379886445586726673163862542768480261054337646592.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:96: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += reg * np.sum(W * W)\n",
      "e:\\miniconda3\\envs\\cs231n\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:96: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += reg * np.sum(W * W)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 900 / 1000: loss inf\n",
      "iteration 0 / 1000: loss 1542.306377\n",
      "iteration 100 / 1000: loss 4318213244407166238874848771654820031092498656290736013925342596064333603387791433088456279171612137327567625978033066737664.000000\n",
      "iteration 200 / 1000: loss 11150705623923679025565234912987146963623634994563612033007113308719595097275781145555555539667029761504259852215560074028538253895795095444777324173249320645513319566769149957751877764063803268131133818558022840615320693845128840448777769713664.000000\n",
      "iteration 300 / 1000: loss inf\n",
      "iteration 400 / 1000: loss inf\n",
      "iteration 500 / 1000: loss inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:116: RuntimeWarning: overflow encountered in multiply\n",
      "  dW += np.dot(X.T, margins)/num_train + 2 * reg * W     # D by C\n",
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:113: RuntimeWarning: invalid value encountered in greater\n",
      "  margins[margins > 0] = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 600 / 1000: loss nan\n",
      "iteration 700 / 1000: loss nan\n",
      "iteration 800 / 1000: loss nan\n",
      "iteration 900 / 1000: loss nan\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.362571 val accuracy: 0.374000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.355286 val accuracy: 0.346000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.074041 val accuracy: 0.061000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.374000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        mySvm = LinearSVM()\n",
    "        loss_hist = mySvm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1000, verbose=True)\n",
    "        y_train_pred = mySvm.predict(X_train)\n",
    "        train_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = mySvm.predict(X_val)\n",
    "        val_accuracy = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_accuracy, val_accuracy)\n",
    "        \n",
    "        if val_accuracy > best_val:\n",
    "            best_val = val_accuracy\n",
    "            best_svm = mySvm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(1e-07, 25000.0): (0.36257142857142854, 0.374), (1e-07, 50000.0): (0.35528571428571426, 0.346), (5e-05, 25000.0): (0.07404081632653062, 0.061), (5e-05, 50000.0): (0.10026530612244898, 0.087)}\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.361000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
